{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "qP-3b4xBohYB",
   "metadata": {
    "id": "qP-3b4xBohYB"
   },
   "source": [
    "**Importing necessary libraries for data visualization and manipulation :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0653577b",
   "metadata": {
    "id": "0653577b"
   },
   "outputs": [],
   "source": [
    "# Importing necessary libraries for data visualization and manipulation\n",
    "\n",
    "# Used for creating static, interactive, and animated visualizations in Python\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Used for creating line markers within plots\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# Provides a way of using operating system dependent functionality like reading or writing to a file system\n",
    "import os\n",
    "\n",
    "# Implements binary protocols for serializing and de-serializing a Python object structure.\n",
    "import pickle\n",
    "\n",
    "# Data manipulation and analysis library\n",
    "import pandas as pd\n",
    "\n",
    "# Fundamental package for scientific computing with Python\n",
    "import numpy as np\n",
    "\n",
    "# Represents a duration, the difference between two dates or times.\n",
    "from datetime import timedelta\n",
    "\n",
    "from scipy import interpolate  # Subpackage for interpolation which provides functions to deal with interpolation algorithms.\n",
    "from scipy import signal\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38133112",
   "metadata": {},
   "source": [
    "**Import ppg data with Nans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c98b603",
   "metadata": {
    "id": "8c98b603"
   },
   "outputs": [],
   "source": [
    "# Define the directory containing the patient files\n",
    "directory = 'C:/Users/adior/Documents/Digital Medical Tech - degree/Final Mop Project/Train Files/Train files with Nan'\n",
    "#directory = 'C:/Users/Idan Lichter/OneDrive/Desktop/study/3rd year/Final Project/Train Files with NaN'\n",
    "\n",
    "# List all CSV files in the directory\n",
    "csv_files = [f for f in os.listdir(directory) if f.endswith('.csv')]\n",
    "\n",
    "# Initialize an empty dictionary to store DataFrames\n",
    "wind_dict = {}\n",
    "\n",
    "# Load each CSV file into a DataFrame and store it in the dictionary\n",
    "for i, file in enumerate(csv_files):\n",
    "    file_path = os.path.join(directory, file)\n",
    "    try:\n",
    "        df = pd.read_csv(file_path)\n",
    "        wind_dict[i] = df\n",
    "    except Exception as e:\n",
    "        print(f\"Failed to load {file_path}: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1e96d1",
   "metadata": {},
   "source": [
    "The goal here is to filter out and retain only those segments that are continuous and have a duration of at least 12 seconds.\n",
    "\n",
    "The data is filtered by first removing any rows where the 'PLETH' values are Nans.\n",
    "The filtered data is then checked for gaps in the timestamp values,if a gap larger than 10 milliseconds is found, the segment is split at that point.\n",
    "Each segment is then checked to ensure it contains at least 1200 rows (since the sampling frequency is 100 Hz, 1200 rows would correspond to 12 seconds).\n",
    "\n",
    "Only the segments that are continuous and at least 12 seconds long are kept, and these are stored in a new dictionary called valid_segments_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a844dcf-c20b-4a8b-a046-15ef9f102168",
   "metadata": {
    "id": "7a844dcf-c20b-4a8b-a046-15ef9f102168"
   },
   "source": [
    "**Filtering Non-NaN Segments that last at least 12-second**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7af9631c",
   "metadata": {
    "id": "7af9631c",
    "outputId": "ab8f19d7-744f-41a5-982d-16d09f8eaab2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of valid segments: 636\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary to store valid segments of PPG data\n",
    "valid_segments_dict = {}\n",
    "# Initialize an index to keep track of the segment number\n",
    "segment_index = 0\n",
    "\n",
    "# Iterate through each DataFrame in wind_dict\n",
    "for file, df in wind_dict.items():\n",
    "    # Filter the DataFrame to retain only rows where 'PLETH' values are not NaN\n",
    "    notna_fd = df[df['PLETH'].notna()]\n",
    "\n",
    "    # Extract the timestamp values from the filtered DataFrame\n",
    "    time_stamps = notna_fd['TIMESTAMP_MS'].values\n",
    "    # Initialize the starting index for the current segment\n",
    "    start_idx = 0\n",
    "    \n",
    "    # Loop through the timestamps to find gaps larger than 10 milliseconds\n",
    "    for i in range(1, len(time_stamps)):\n",
    "        gap_size = time_stamps[i] - time_stamps[i-1]\n",
    "        # If a gap larger than 10 milliseconds is found\n",
    "        if gap_size > 10:\n",
    "            # Check if the segment length is at least 1200-second\n",
    "            if len(notna_fd.iloc[start_idx:i]) >= 1200:\n",
    "                # If valid, add the segment to valid_segments_dict\n",
    "                valid_segments_dict[segment_index] = notna_fd.iloc[start_idx:i]\n",
    "                # Increment the segment index for the next segment\n",
    "                segment_index += 1\n",
    "            # Update the start index to the current position after the gap\n",
    "            start_idx = i\n",
    "\n",
    "    # After the loop, check the last segment for validity (length >= 1200)\n",
    "    if len(notna_fd.iloc[start_idx:]) >= 1200:\n",
    "        # Add the last segment if valid\n",
    "        valid_segments_dict[segment_index] = notna_fd.iloc[start_idx:]\n",
    "        segment_index += 1\n",
    "\n",
    "# Print the number of valid segments that have been identified and stored\n",
    "print(f\"Number of valid segments: {len(valid_segments_dict)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2652f09e",
   "metadata": {
    "id": "2652f09e"
   },
   "source": [
    "# Pre-processing\n",
    "Pre-processing of PPG signal segments includes smoothing the signal, finding cycles using the peak points in each cycle, two-dimensional normalization of PPG signals. Also, rejecting low-quality segments and cutting the segments so that they contain complete waves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6fe28003",
   "metadata": {
    "id": "6fe28003"
   },
   "outputs": [],
   "source": [
    "def FindProperWindows(PPG, Windows_Table, key, start_window_id, proper_windows):\n",
    "    \"\"\"\n",
    "    Identifies and processes proper windows from the PPG data.\n",
    "\n",
    "    Parameters:\n",
    "    - PPG (DataFrame): The PPG signal data.\n",
    "    - Windows_Table (DataFrame): The table to store window information and results.\n",
    "    - key (int): The key identifying the current PPG segment.\n",
    "    - start_window_id (int): The starting window_id for this segment.\n",
    "    - proper_windows (dict): Dictionary to store the proper windows identified.\n",
    "\n",
    "    Returns:\n",
    "    - DataFrame: The updated Windows_Table with proper window information.\n",
    "    - int: The updated window_id to continue from for the next segment.\n",
    "    \"\"\"\n",
    "    window_size = 12 * 100  # 12 seconds window with fs=100\n",
    "    start_idx = 0\n",
    "    window_id = start_window_id\n",
    "    windows_list = []\n",
    "\n",
    "    while start_idx + window_size <= len(PPG):\n",
    "        wind = GetWindow(PPG, start_idx, window_id, key)\n",
    "        wind = WindowFullProcess(wind, by_value=True)\n",
    "\n",
    "        # Append information to list\n",
    "        window_info = {\n",
    "            'window_id': window_id,\n",
    "            'segment_index': key,\n",
    "            'start_index': start_idx,\n",
    "            'end_index': start_idx + window_size,\n",
    "            'ProperWindow': False  # Default value, will be updated later\n",
    "        }\n",
    "        windows_list.append(window_info)\n",
    "\n",
    "        Rejecting_Window(wind, windows_list[-1], window_id)\n",
    "\n",
    "        if windows_list[-1]['ProperWindow']:\n",
    "            wind['window_index'] = window_id\n",
    "            proper_windows[window_id] = wind  # Store the proper window directly in the dictionary\n",
    "            start_idx += 7 * 100  # move 7 seconds forward\n",
    "        else:\n",
    "            min_peaks, _ = Get_Cycles_Peaks(wind)\n",
    "            if len(min_peaks) > 0:\n",
    "                if start_idx == min_peaks[0]:\n",
    "                    start_idx = min_peaks[1]\n",
    "                else:\n",
    "                    start_idx = min_peaks[0]\n",
    "            else:\n",
    "                start_idx += window_size\n",
    "\n",
    "        # Increment the window_id for the next iteration\n",
    "        window_id += 1\n",
    "\n",
    "    # Convert list to DataFrame and concatenate\n",
    "    new_windows = pd.DataFrame(windows_list)\n",
    "    Windows_Table = pd.concat([Windows_Table, new_windows], ignore_index=True)\n",
    "\n",
    "    Windows_Table['ProperWindow'] = Windows_Table['ProperWindow'].astype(bool)\n",
    "    return Windows_Table, window_id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "baae74a4",
   "metadata": {
    "id": "baae74a4"
   },
   "outputs": [],
   "source": [
    "def Reset_Column(Table, col_name, col_ind, default):\n",
    "    \"\"\"\n",
    "    Resets a column in the DataFrame to a specified default value.\n",
    "\n",
    "    Parameters:\n",
    "    - Table (DataFrame): The DataFrame to update.\n",
    "    - col_name (str): The name of the column to reset.\n",
    "    - col_ind (int): The index position to insert the column if it does not exist.\n",
    "    - default: The default value to set for the column.\n",
    "\n",
    "    \"\"\"\n",
    "    if col_name not in Table.columns:\n",
    "        Table.insert(col_ind, col_name, default)\n",
    "    else:\n",
    "        Table[col_name] = default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8f2e7da1-4a74-4467-a9f9-a337a8626e7b",
   "metadata": {
    "id": "8f2e7da1-4a74-4467-a9f9-a337a8626e7b"
   },
   "outputs": [],
   "source": [
    "def GetWindow(PPG, start_idx, window_id, segment_index, window_size=12*100):  # Assuming fs=100\n",
    "    \"\"\"\n",
    "    Extracts a window of data from the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - PPG (DataFrame): The PPG signal data.\n",
    "    - start_idx (int): The starting index for the window.\n",
    "    - window_size (int): The size of the window (default is 1200, for 12 seconds at 100 Hz).\n",
    "\n",
    "    Returns:\n",
    "    - DataFrame: The extracted window of PPG data.\n",
    "    \"\"\"\n",
    "    end_idx = start_idx + window_size\n",
    "    return PPG.iloc[start_idx:end_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c23bc70",
   "metadata": {},
   "source": [
    "**Each 12-second window undergoes several signal processing steps to clean and refine the data:**\n",
    "\n",
    "Noise Filtering: A low-pass filter is applied to remove high-frequency noise.\n",
    "\n",
    "Trend Removal: A high-pass filter is used to remove trends from the signal, leaving behind the oscillatory components that represent the heartbeats.\n",
    "\n",
    "Auto-correlation: This measures the similarity of the signal to itself over time, helping identify regular patterns or cycles within the window.\n",
    "\n",
    "Peak Detection: The function detects peaks (maxima and minima) in the signal that correspond to heartbeats. It uses methods like SDET (Successive Decomposition and Extraction of Peaks) for accurate detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eec2c696-72c1-4b23-84cf-5ad55f9d1345",
   "metadata": {
    "id": "eec2c696-72c1-4b23-84cf-5ad55f9d1345"
   },
   "outputs": [],
   "source": [
    "def WindowFullProcess(wind, by_value=False, wind_id=None, fs=100, padding=250):\n",
    "    \"\"\"\n",
    "    Processes a window of PPG data by applying various filters and transformations.\n",
    "\n",
    "    Parameters:\n",
    "    - wind (DataFrame): The window of PPG data to process.\n",
    "    - by_value (bool): If True, processes a copy of the input data (default is False).\n",
    "    - wind_id (optional): Identifier for the window.\n",
    "    - fs (int): Sampling frequency (default is 100).\n",
    "    - padding (int): Padding value for auto-correlation (default is 250).\n",
    "\n",
    "    Returns:\n",
    "    - DataFrame: The processed window of PPG data.\n",
    "    \"\"\"\n",
    "    if by_value:\n",
    "        wind = wind.copy()\n",
    "\n",
    "    PLETH = wind['PLETH']\n",
    "\n",
    "    # Initialize DataTime if not present\n",
    "    if 'DataTime' not in wind.columns:\n",
    "        wind['DataTime'] = np.arange(len(wind)) / fs\n",
    "\n",
    "    # Apply butter - lowpass filter:\n",
    "    filtered = Segment_Filter(PLETH, ftype='low', fs=fs)\n",
    "    wind['F_PLETH'] = filtered\n",
    "\n",
    "    # Removes the trend (Highpass filter) from the PPG signal:\n",
    "    high_detrended = Segment_Trend_Removel(filtered, ftype='high', fs=fs)\n",
    "    wind['HiDeTr_PLETH'] = high_detrended\n",
    "\n",
    "    # Computes the auto-correlation of the PPG signal without normalization:\n",
    "    auto_correlation = Segment_AutoCorrelation(high_detrended, normalized=False, padding=int(1*fs)) # 1*fs=1[sec]\n",
    "    wind['AuCo_PLETH'] = auto_correlation\n",
    "\n",
    "    # Computes the auto-correlation of the PPG signal with normalization:\n",
    "    auto_correlation = Segment_AutoCorrelation(high_detrended, normalized=True, padding=int(1*fs)) # 1*fs=1[sec]\n",
    "    wind['NoAuCo_PLETH'] = auto_correlation\n",
    "\n",
    "    # Detects min and max peaks in the PPG signal.\n",
    "    get_peaks(wind, y_name='HiDeTr_PLETH', is_window=True, method='SDET', padding=int(0.5*fs)) # 0.5*fs=2[sec]\n",
    "\n",
    "    # Tests if the window contains consistent extremum points (maxima and minima).\n",
    "    max_min_test(wind, to_raise=True)\n",
    "\n",
    "    if by_value:\n",
    "        return wind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "06a933ba-1ffd-4752-8f71-3e2d2797c562",
   "metadata": {
    "id": "06a933ba-1ffd-4752-8f71-3e2d2797c562"
   },
   "outputs": [],
   "source": [
    "def Segment_Filter(y, ftype='low', fs=100):\n",
    "    \"\"\"\n",
    "    Applies a filter to the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - y (Series): The PPG signal to filter.\n",
    "    - ftype (str): The type of filter to apply ('low' for lowpass, 'SG' for Savitzky-Golay).\n",
    "    - fs (int): Sampling frequency (default is 100).\n",
    "\n",
    "    Returns:\n",
    "    - ndarray: The filtered PPG signal.\n",
    "    \"\"\"\n",
    "    if ftype == 'SG':\n",
    "        filtered = signal.savgol_filter(y, window_length=19, polyorder=4)\n",
    "    elif ftype == 'low':\n",
    "        b, a = signal.butter(5, 10, 'lowpass', fs=fs)\n",
    "        filtered = signal.filtfilt(b, a, y)\n",
    "    return filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "229a2874-7074-42bc-b278-4b6612312e1b",
   "metadata": {
    "id": "229a2874-7074-42bc-b278-4b6612312e1b"
   },
   "outputs": [],
   "source": [
    "def Segment_Trend_Removel(y, ftype='linar', fs=100):\n",
    "    \"\"\"\n",
    "    Removes the trend from the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - y (Series): The PPG signal to detrend.\n",
    "    - ftype (str): The type of detrending to apply ('linar' for linear, 'high' for highpass).\n",
    "    - fs (int): Sampling frequency (default is 100).\n",
    "\n",
    "    Returns:\n",
    "    - ndarray: The detrended PPG signal.\n",
    "    \"\"\"\n",
    "    if ftype == 'linar':\n",
    "        detrended = signal.detrend(y)\n",
    "    elif ftype == 'high':\n",
    "        b, a = signal.butter(5, 0.6, 'highpass', fs=fs)\n",
    "        detrended = signal.filtfilt(b, a, y)\n",
    "    return detrended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e8b5df94-de4c-4fe7-a333-f31fc190f2ae",
   "metadata": {
    "id": "e8b5df94-de4c-4fe7-a333-f31fc190f2ae"
   },
   "outputs": [],
   "source": [
    "def Segment_AutoCorrelation(y, normalized=False, padding=2.5*100):\n",
    "    \"\"\"\n",
    "    Computes the auto-correlation of the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - y (Series): The PPG signal to auto-correlate.\n",
    "    - normalized (bool): If True, normalizes the auto-correlation (default is False).\n",
    "    - padding (int): Padding value for auto-correlation (default is 250).\n",
    "\n",
    "    Returns:\n",
    "    - Series: The auto-correlated PPG signal with padding.\n",
    "    \"\"\"\n",
    "    cuted_y = y[int(padding//2):-int(padding//2)]\n",
    "\n",
    "    corr = signal.correlate(cuted_y, cuted_y, mode='same')\n",
    "    if normalized:\n",
    "        a = np.arange(1, corr.size + 1)\n",
    "        f = 1 - corr.size % 2\n",
    "        b = np.concatenate([a, a[-2::-1]])[corr.size // 2 - f:-corr.size // 2]\n",
    "        corr /= b\n",
    "    corr /= corr.max()\n",
    "    paded_corr = y.copy()\n",
    "    paded_corr[:int(padding//2)] = np.nan\n",
    "    paded_corr[-int(padding//2):] = np.nan\n",
    "    paded_corr[int(padding//2):-int(padding//2)] = corr\n",
    "    return paded_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f705a6eb-54d7-467a-8a9d-2648fd7c55d4",
   "metadata": {
    "id": "f705a6eb-54d7-467a-8a9d-2648fd7c55d4"
   },
   "outputs": [],
   "source": [
    "def Patient_max_peaks(patient_PPG, y_name='HiDeTr_PLETH', is_window = False, padding=250, method='SDET'):\n",
    "    \"\"\"\n",
    "    Detects maximum peaks in the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - patient_PPG (DataFrame): The PPG data.\n",
    "    - y_name (str): The name of the column containing the PPG signal.\n",
    "    - is_window (bool): If True, processes the data as a window.\n",
    "    - padding (int): Padding value for peak detection (default is 250).\n",
    "    - method (str): The method for peak detection ('SDET' or 'hp').\n",
    "\n",
    "    \"\"\"\n",
    "    if 'is_max_peak' not in patient_PPG.columns:\n",
    "        col_ind = int(np.argmax(patient_PPG.columns == y_name))\n",
    "        patient_PPG.insert(col_ind, 'is_max_peak', False)\n",
    "    else:\n",
    "        patient_PPG['is_max_peak'] = False\n",
    "\n",
    "    if is_window:\n",
    "        continuous_groups = [list(patient_PPG.index)]\n",
    "    else:\n",
    "        print('Didnt get proper window')\n",
    "\n",
    "    for i, index in enumerate(continuous_groups):\n",
    "\n",
    "        y = patient_PPG.loc[index, y_name]\n",
    "\n",
    "        max_peaks = Segment_max_peaks(y, method=method)\n",
    "        if len(max_peaks):\n",
    "            patient_PPG.loc[max_peaks, 'is_max_peak'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ddfc431f-99df-499f-b32c-981a670d5bd9",
   "metadata": {
    "id": "ddfc431f-99df-499f-b32c-981a670d5bd9"
   },
   "outputs": [],
   "source": [
    "def Segment_max_peaks(y, fs=100, method='SDET'):\n",
    "    \"\"\"\n",
    "    Detects maximum peaks in a segment of the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - y (Series): The PPG signal.\n",
    "    - fs (int): Sampling frequency (default is 100).\n",
    "    - method (str): The method for peak detection ('SDET' or 'hp').\n",
    "\n",
    "    Returns:\n",
    "    - ndarray: Indices of the maximum peaks.\n",
    "    \"\"\"\n",
    "    if method == 'hp':\n",
    "        try:\n",
    "            warnings.filterwarnings(\"ignore\")\n",
    "            working_data, _ = hp.process(y.values, fs)\n",
    "            warnings.filterwarnings(\"default\")\n",
    "        except:\n",
    "            max_peaks = np.array([])\n",
    "        else:\n",
    "            # Extract the 'peaklist' from the 'working_data' dictionary.\n",
    "            max_peaks = np.array(working_data['peaklist'])\n",
    "            max_peaks = y.index[max_peaks]\n",
    "    elif method == 'SDET':\n",
    "        maximum_heart_beat_frequency = 1.5 # [Hz]1.5 is lower the 1.67 Hz which is 100 bit per min which is the maximum hart bit\n",
    "        maximum_signal_frequency = 10 #[Hz]\n",
    "        steps = 10 - 1\n",
    "        lowpass_fre_arr = np.linspace(maximum_heart_beat_frequency, maximum_signal_frequency, steps, endpoint=False)\n",
    "        half_wind_size = int(1/(100/60)/4*fs) #[(1/(bit/min))/4*fs] = [(1/max_frec)/4*fs]\n",
    "        peaks_indexes = SDET(y.values, lowpass_fre_arr, half_wind_size, fs=fs)\n",
    "        if len(peaks_indexes):\n",
    "            max_peaks = y.index[peaks_indexes]\n",
    "        else:\n",
    "            max_peaks = np.array([])\n",
    "\n",
    "    return max_peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c6f3d7ab",
   "metadata": {
    "id": "c6f3d7ab"
   },
   "outputs": [],
   "source": [
    "def SDET(y, lowpass_fre_arr, half_wind_size, fs=100, visual=False):\n",
    "    \"\"\"\n",
    "    Applies the Successive Decomposition and Extraction of Peaks (SDET) method to identify peaks in the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - y (ndarray): The PPG signal to process.\n",
    "    - lowpass_fre_arr (ndarray): Array of lowpass filter frequencies for successive filtering.\n",
    "    - half_wind_size (int): Half the size of the window used for local peak detection.\n",
    "    - fs (int): Sampling frequency (default is 100).\n",
    "    - visual (bool): If True, visualizes the filtering and peak detection steps (default is False).\n",
    "\n",
    "    Returns:\n",
    "    - ndarray: The indices of the identified peaks in the PPG signal.\n",
    "    \"\"\"\n",
    "    filtered_arr = []\n",
    "    for lowpass_fre in lowpass_fre_arr:\n",
    "        b, a = signal.butter(5, lowpass_fre, 'lowpass', fs=fs)\n",
    "        filtered = signal.filtfilt(b, a, y)\n",
    "        filtered_arr.append(filtered)\n",
    "    filtered_arr.append(y)\n",
    "    current_peaks_indexes, _ = signal.find_peaks(filtered_arr[0])\n",
    "\n",
    "    if visual:\n",
    "        t = np.arange(y.size)\n",
    "        plt.figure()\n",
    "        plt.plot(t, y)\n",
    "        plt.plot(t, filtered_arr[0])\n",
    "        plt.plot(t[current_peaks_indexes], filtered_arr[0][current_peaks_indexes], '.k')\n",
    "        plt.title(f'step: {0}   LPF: {lowpass_fre_arr[0]:.2f}[Hz]')\n",
    "\n",
    "    for step_i, filtered in enumerate(filtered_arr[1:], 1):\n",
    "        last_peaks_indexes = np.array(current_peaks_indexes, dtype=int)\n",
    "\n",
    "        potential_peaks_indexes, _ = signal.find_peaks(filtered)\n",
    "\n",
    "        filtered_gradient = np.gradient(filtered)\n",
    "        direction = np.sign(filtered_gradient[last_peaks_indexes])\n",
    "\n",
    "        mid_peaks_indexes = []\n",
    "        for d_i, p_i in zip(direction, last_peaks_indexes):\n",
    "            dis = np.abs(potential_peaks_indexes - p_i)\n",
    "            chosen_peak = None\n",
    "            if dis.min() == 0:\n",
    "                chosen_peak = potential_peaks_indexes[dis.argmin()]\n",
    "            else:\n",
    "                closest_peak_ind = potential_peaks_indexes[dis.argmin()]\n",
    "                if closest_peak_ind - p_i > 0:\n",
    "                    closest_peaks_ind = [dis.argmin()-1, dis.argmin()]\n",
    "                else:\n",
    "                    closest_peaks_ind = [dis.argmin(), dis.argmin()+1]\n",
    "                if closest_peaks_ind[0] < 0:\n",
    "                    if d_i == 1:\n",
    "                        closest_peak_ind = closest_peaks_ind[1]\n",
    "                        chosen_peak = potential_peaks_indexes[closest_peak_ind]\n",
    "                elif closest_peaks_ind[1] >= len(potential_peaks_indexes):\n",
    "                    if d_i == -1:\n",
    "                        closest_peak_ind = closest_peaks_ind[0]\n",
    "                        chosen_peak = potential_peaks_indexes[closest_peak_ind]\n",
    "                else:\n",
    "                    closest_peaks = potential_peaks_indexes[closest_peaks_ind]\n",
    "                    chosen_peak = closest_peaks[int(d_i == 1)]\n",
    "            if chosen_peak is not None:\n",
    "                mid_peaks_indexes.append(chosen_peak)\n",
    "        mid_peaks_indexes = np.unique(mid_peaks_indexes)\n",
    "\n",
    "        current_peaks_indexes = []\n",
    "        for m_i in mid_peaks_indexes:\n",
    "            logical_left = potential_peaks_indexes >= m_i - half_wind_size\n",
    "            logical_right = potential_peaks_indexes <= m_i + half_wind_size\n",
    "            bool_ind = np.logical_and(logical_left, logical_right)\n",
    "            local_peaks = potential_peaks_indexes[bool_ind]\n",
    "            chosen_peak = local_peaks[np.argmax(filtered[local_peaks])]\n",
    "            current_peaks_indexes.append(chosen_peak)\n",
    "        current_peaks_indexes = np.unique(current_peaks_indexes)\n",
    "\n",
    "        if visual:\n",
    "            plt.figure()\n",
    "            if step_i < len(lowpass_fre_arr):\n",
    "                lowpass_fre = lowpass_fre_arr[step_i]\n",
    "                plt.plot(t, y)\n",
    "                plt.title(f'step: {step_i}   LPF: {lowpass_fre:.2f}[Hz]')\n",
    "            else:\n",
    "                plt.title('Final Results')\n",
    "            plt.plot(t, filtered)\n",
    "            plt.plot(t[last_peaks_indexes], filtered[last_peaks_indexes], 'xb')\n",
    "            plt.plot(t[potential_peaks_indexes], filtered[potential_peaks_indexes], 'oy')\n",
    "            plt.plot(t[mid_peaks_indexes], filtered[mid_peaks_indexes], '*b')\n",
    "            plt.plot(t[current_peaks_indexes], filtered[current_peaks_indexes], '.k')\n",
    "    return current_peaks_indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d35063e7-3f80-406f-80f5-05ea74e2830b",
   "metadata": {
    "id": "d35063e7-3f80-406f-80f5-05ea74e2830b"
   },
   "outputs": [],
   "source": [
    "def Patient_min_peaks(patient_PPG, y_name='HiDeTr_PLETH', is_window = False, padding = 250):\n",
    "    \"\"\"\n",
    "    Detects minimum peaks in the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - patient_PPG (DataFrame): The PPG data.\n",
    "    - y_name (str): The name of the column containing the PPG signal.\n",
    "    - is_window (bool): If True, processes the data as a window (default is False).\n",
    "    - padding (int): Padding value for peak detection (default is 250).\n",
    "\n",
    "    \"\"\"\n",
    "    if 'is_min_peak' not in patient_PPG.columns:\n",
    "        col_ind = int(np.argmax(patient_PPG.columns == 'is_max_peak'))\n",
    "        patient_PPG.insert(col_ind, 'is_min_peak', False)\n",
    "    else:\n",
    "        patient_PPG['is_min_peak'] = False\n",
    "\n",
    "    if is_window:\n",
    "        continuous_groups = [list(patient_PPG.index)]\n",
    "    else:\n",
    "        print('Didnt get proper window')\n",
    "\n",
    "    for index in continuous_groups:\n",
    "\n",
    "        y = patient_PPG.loc[index, y_name]\n",
    "\n",
    "        is_max_peak = patient_PPG.loc[index, 'is_max_peak']\n",
    "\n",
    "        max_peaks_index = is_max_peak[is_max_peak].index\n",
    "        if len(max_peaks_index):\n",
    "            min_peaks = Segment_min_peaks(y, max_peaks_index)\n",
    "\n",
    "            if len(min_peaks):\n",
    "                patient_PPG.loc[min_peaks, 'is_min_peak'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6f211948-400a-4fec-9486-bff334050360",
   "metadata": {
    "id": "6f211948-400a-4fec-9486-bff334050360"
   },
   "outputs": [],
   "source": [
    "def Segment_min_peaks(y, max_peaks_index):\n",
    "    \"\"\"\n",
    "    Detects minimum peaks in a segment of the PPG signal between maximum peaks.\n",
    "\n",
    "    Parameters:\n",
    "    - y (Series): The PPG signal.\n",
    "    - max_peaks_index (list): Indices of the maximum peaks.\n",
    "\n",
    "    Returns:\n",
    "    - ndarray: Indices of the minimum peaks.\n",
    "    \"\"\"\n",
    "    min_peaks = []\n",
    "\n",
    "    first_point_ind = y.index[0]\n",
    "    last_point_ind = y.index[-1]\n",
    "\n",
    "    max_peaks_index = list(max_peaks_index)\n",
    "    if first_point_ind < max_peaks_index[0]:\n",
    "        max_peaks_index.insert(0, first_point_ind)\n",
    "    if last_point_ind > max_peaks_index[-1]:\n",
    "        max_peaks_index.append(last_point_ind)\n",
    "\n",
    "    # Iterates over pairs of adjacent maximum peaks using a loop.\n",
    "    for peak0_i, peak1_i in zip(max_peaks_index[:-1], max_peaks_index[1:]):\n",
    "        # Find the index of the minimum value within the corresponding data range between the two maximum peaks and appends\n",
    "        # it to 'min_peaks'.\n",
    "\n",
    "        subsegment_i = y.loc[peak0_i:peak1_i]\n",
    "        potential_min_peaks_indexes, _ = signal.find_peaks(-subsegment_i)\n",
    "        if len(potential_min_peaks_indexes):\n",
    "\n",
    "            potential_min_peaks_indexes = subsegment_i.index[potential_min_peaks_indexes]\n",
    "            right_potential_min_index = max(potential_min_peaks_indexes)\n",
    "            right_potential_min = subsegment_i.loc[right_potential_min_index]\n",
    "\n",
    "\n",
    "\n",
    "            if right_potential_min < y.loc[[peak0_i, peak1_i]].min():\n",
    "                min_peaks.append(right_potential_min_index)\n",
    "\n",
    "    min_peaks = np.array(min_peaks)\n",
    "    return min_peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a069cfd0-c3d3-4bf7-b218-33292ef2489f",
   "metadata": {
    "id": "a069cfd0-c3d3-4bf7-b218-33292ef2489f"
   },
   "outputs": [],
   "source": [
    "def Patient_reject_invalid_max_peaks(patient_PPG, y_name='HiDeTr_PLETH', is_window = False):\n",
    "    \"\"\"\n",
    "    Rejects invalid maximum peaks in the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - patient_PPG (DataFrame): The PPG data.\n",
    "    - y_name (str): The name of the column containing the PPG signal.\n",
    "    - is_window (bool): If True, processes the data as a window (default is False).\n",
    "\n",
    "    \"\"\"\n",
    "    if is_window:\n",
    "        continuous_groups = [list(patient_PPG.index)]\n",
    "    else:\n",
    "        print('Didnt get proper window')\n",
    "\n",
    "    for index in continuous_groups:\n",
    "\n",
    "        y = patient_PPG.loc[index, y_name]\n",
    "\n",
    "        is_min_peak = patient_PPG.loc[index, 'is_min_peak']\n",
    "        is_max_peak = patient_PPG.loc[index, 'is_max_peak']\n",
    "        max_peaks_index = is_max_peak[is_max_peak].index\n",
    "\n",
    "        if len(max_peaks_index):\n",
    "            index2reject = Segment_reject_invalid_max_peaks(y, max_peaks_index, is_min_peak)\n",
    "            if len(index2reject):\n",
    "                patient_PPG.loc[index2reject, 'is_max_peak'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1a3a40c1-68ce-4dec-ad4b-5b18c5164107",
   "metadata": {
    "id": "1a3a40c1-68ce-4dec-ad4b-5b18c5164107"
   },
   "outputs": [],
   "source": [
    "def Segment_reject_invalid_max_peaks(y, max_peaks_index, is_min_peak):\n",
    "    \"\"\"\n",
    "    Identifies invalid maximum peaks in a segment of the PPG signal.\n",
    "\n",
    "    Parameters:\n",
    "    - y (Series): The PPG signal.\n",
    "    - max_peaks_index (list): Indices of the maximum peaks.\n",
    "    - is_min_peak (Series): Boolean series indicating the positions of minimum peaks.\n",
    "\n",
    "    Returns:\n",
    "    - list: Indices of the invalid maximum peaks to be rejected.\n",
    "    \"\"\"\n",
    "    current_min_exist = 1\n",
    "    potantional_index2reject = []\n",
    "    for max_peak0_i, max_peak1_i in zip(max_peaks_index[:-1], max_peaks_index[1:]):\n",
    "\n",
    "        previous_min_exist = current_min_exist\n",
    "        current_min_exist = is_min_peak.loc[max_peak0_i: max_peak1_i].sum()\n",
    "\n",
    "        if current_min_exist == 0:\n",
    "            if previous_min_exist == 0:\n",
    "                potantional_index2reject[-1].update({max_peak0_i, max_peak1_i})\n",
    "            else:\n",
    "                potantional_index2reject.append({max_peak0_i, max_peak1_i})\n",
    "\n",
    "    index2reject = []\n",
    "    for ind_set in potantional_index2reject:\n",
    "        ind = np.array(sorted(list(ind_set))) #Potential indexes of rejection\n",
    "        index2reject_temp = y.loc[ind].argsort().values[:-1]\n",
    "        index2reject.extend(ind[index2reject_temp])\n",
    "\n",
    "    return index2reject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cb830bf2-3be2-4662-859b-c399c34d8dc1",
   "metadata": {
    "id": "cb830bf2-3be2-4662-859b-c399c34d8dc1"
   },
   "outputs": [],
   "source": [
    "def max_min_test(wind, to_raise=False):\n",
    "    \"\"\"\n",
    "    Tests if the window contains consistent extremum points (maxima and minima).\n",
    "\n",
    "    Parameters:\n",
    "    - wind (DataFrame): The window of PPG data.\n",
    "    - to_raise (bool): If True, raises an exception if the test fails (default is False).\n",
    "\n",
    "    Returns:\n",
    "    - bool: True if the extremum points are consistent, False otherwise.\n",
    "    \"\"\"\n",
    "    is_max_peak = wind['is_max_peak']\n",
    "    is_min_peak = wind['is_min_peak']\n",
    "\n",
    "    max_as_2 = is_max_peak.astype('int')*2\n",
    "    min_as_1 = is_min_peak.astype('int')\n",
    "    peaks = min_as_1 + max_as_2\n",
    "\n",
    "    only_peaks = peaks[peaks!=0]\n",
    "    cond = (only_peaks.diff().abs().iloc[1:] == 1).all()\n",
    "    if to_raise:\n",
    "        if not cond:\n",
    "            raise Exception(\"max_min_test: the window includes inconsistent extremum points\")\n",
    "\n",
    "    else:\n",
    "        return cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b02d05be-acb4-4baf-8ed6-a9981df39689",
   "metadata": {
    "id": "b02d05be-acb4-4baf-8ed6-a9981df39689"
   },
   "outputs": [],
   "source": [
    "def Rejecting_Window(PPG_window, window_info, window_id):\n",
    "    \"\"\"\n",
    "    Evaluates if a window of PPG data should be rejected based on various distortion criteria.\n",
    "\n",
    "    Parameters:\n",
    "    - PPG_window (DataFrame): The window of PPG data.\n",
    "    - window_info (dict): The dictionary to store window evaluation results.\n",
    "    - window_id (int): Identifier for the window.\n",
    "    \"\"\"\n",
    "    cycles_number = Get_Cycles_Number(PPG_window)\n",
    "    window_info['Cycles_Number'] = cycles_number\n",
    "    if cycles_number >= 4:\n",
    "\n",
    "        distorted_pearson, pearson_mean = Distorted_By_Corr_Min_Mean(PPG_window['is_min_peak'], PPG_window['HiDeTr_PLETH'], th=0.4)\n",
    "        window_info['Corr_Min_Mean'] = pearson_mean\n",
    "        window_info['Distorted_By_Corr_Min_Mean'] = distorted_pearson\n",
    "\n",
    "        distorted_autocorr, distorted_max, distorted_min = Distorted_By_Autocorr(PPG_window, pearson_mean, th=0.7)\n",
    "        window_info['Autocorr_Max_Cond'] = distorted_max\n",
    "        window_info['Autocorr_Min_Cond'] = distorted_min\n",
    "        window_info['Distorted_By_Autocorr'] = distorted_autocorr\n",
    "\n",
    "        window_info['ProperWindow'] = not (distorted_autocorr or distorted_pearson)\n",
    "\n",
    "    else:\n",
    "        window_info['ProperWindow'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6171a85c-80ec-4dfc-a58a-593285f80f25",
   "metadata": {
    "id": "6171a85c-80ec-4dfc-a58a-593285f80f25"
   },
   "outputs": [],
   "source": [
    "def Get_Cycles_Number(PPG_window):\n",
    "    \"\"\"\n",
    "    Calculates the number of cycles (maximum peaks) in a window of PPG data.\n",
    "\n",
    "    Parameters:\n",
    "    - PPG_window (DataFrame): The window of PPG data.\n",
    "\n",
    "    Returns:\n",
    "    - int: The number of cycles (maximum peaks) in the PPG window.\n",
    "    \"\"\"\n",
    "    _, max_peak = Get_Cycles_Peaks(PPG_window)\n",
    "    return len(max_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "38cad885",
   "metadata": {
    "id": "38cad885"
   },
   "outputs": [],
   "source": [
    "def Get_Cycles_Peaks(PPG_window):\n",
    "    \"\"\"\n",
    "    Identifies the indices of maximum and minimum peaks in a window of PPG data.\n",
    "\n",
    "    Parameters:\n",
    "    - PPG_window (DataFrame): The window of PPG data.\n",
    "\n",
    "    Returns:\n",
    "    - tuple: Two lists containing the indices of the minimum and maximum peaks, respectively.\n",
    "    \"\"\"\n",
    "    max_min_test(PPG_window, to_raise=True)\n",
    "\n",
    "    is_max_peak = PPG_window['is_max_peak']\n",
    "    max_peak = is_max_peak[is_max_peak].index.to_list()\n",
    "\n",
    "    is_min_peak = PPG_window['is_min_peak']\n",
    "    min_peak = is_min_peak[is_min_peak].index.to_list()\n",
    "\n",
    "    if len(min_peak) >= 2 and len(max_peak) >= 1:\n",
    "        if len(min_peak) == len(max_peak)+1:\n",
    "            pass\n",
    "        elif len(min_peak) == len(max_peak)-1:\n",
    "            max_peak = max_peak[1:-1]\n",
    "        elif len(min_peak) == len(max_peak) and min_peak[0] < max_peak[0]:\n",
    "            max_peak = max_peak[:-1]\n",
    "        elif len(min_peak) == len(max_peak) and min_peak[0] > max_peak[0]:\n",
    "            max_peak = max_peak[1:]\n",
    "        else:\n",
    "            raise Exception(\"Get_Cycles_Peaks: the window includes inconsistent extremum points\")\n",
    "    else:\n",
    "        min_peak = []  # empty list\n",
    "        max_peak = []  # empty list\n",
    "\n",
    "    return min_peak, max_peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "baaf6ed0-1a90-4eb5-9964-f1c3e021d372",
   "metadata": {
    "id": "baaf6ed0-1a90-4eb5-9964-f1c3e021d372"
   },
   "outputs": [],
   "source": [
    "def Distorted_By_Corr_Min_Mean(is_min_peak, HiDeTr, th=0.4, padding=100, t_axis=np.arange(1000)):\n",
    "    \"\"\"\n",
    "    Checks if the PPG signal is distorted based on the correlation of normalized cycles.\n",
    "\n",
    "    Parameters:\n",
    "    - is_min_peak (Series): Boolean series indicating the positions of minimum peaks.\n",
    "    - HiDeTr (Series): Detrended PPG signal.\n",
    "    - th (float): Threshold for correlation (default is 0.4).\n",
    "    - padding (int): Padding value for correlation (default is 100).\n",
    "    - t_axis (ndarray): Time axis for interpolation (default is np.arange(1000)).\n",
    "\n",
    "    Returns:\n",
    "    - tuple: Boolean indicating if the signal is distorted and the minimum mean correlation value.\n",
    "    \"\"\"\n",
    "    is_min_peak = is_min_peak.iloc[padding // 2 : -padding // 2]\n",
    "    HiDeTr = HiDeTr.iloc[padding // 2 : -padding // 2]\n",
    "\n",
    "    min_peak_index = is_min_peak[is_min_peak].index\n",
    "\n",
    "    distorted = False\n",
    "\n",
    "    normalized_cycles = []\n",
    "    for i0, i1 in zip(min_peak_index[:-1], min_peak_index[1:]):\n",
    "        sub_HiDeTr = HiDeTr.loc[i0:i1].copy()\n",
    "\n",
    "        f = interpolate.interp1d(t_axis[:sub_HiDeTr.size], sub_HiDeTr, kind=\"cubic\")\n",
    "        new_t_axis = np.linspace(0, sub_HiDeTr.size - 1, 100)\n",
    "        resampleed_sub_HiDeTr = f(new_t_axis)\n",
    "\n",
    "        resampleed_sub_HiDeTr -= resampleed_sub_HiDeTr.min()\n",
    "        resampleed_sub_HiDeTr /= resampleed_sub_HiDeTr.max()\n",
    "\n",
    "        normalized_cycles.append(resampleed_sub_HiDeTr)\n",
    "\n",
    "    correlation_matrix = np.zeros((len(normalized_cycles), len(normalized_cycles)))\n",
    "    for i, y1 in enumerate(normalized_cycles):\n",
    "        for j, y2 in enumerate(normalized_cycles):\n",
    "            r, _ = pearsonr(y1, y2)\n",
    "            correlation_matrix[i, j] = r\n",
    "\n",
    "    pearson_mean = (correlation_matrix.sum(axis=0) - 1) / (correlation_matrix.shape[0] - 1)\n",
    "    pearson_min_mean = pearson_mean.min()\n",
    "    distorted = pearson_min_mean < th\n",
    "\n",
    "    return distorted, pearson_min_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ec87a855-ded1-4d58-9cb2-7a3087e95a49",
   "metadata": {
    "id": "ec87a855-ded1-4d58-9cb2-7a3087e95a49"
   },
   "outputs": [],
   "source": [
    "def Distorted_By_Autocorr(wind, pearson_min_mean, th=0.7):\n",
    "    \"\"\"\n",
    "    Checks if the PPG signal is distorted based on auto-correlation and minimum-mean correlation.\n",
    "\n",
    "    Parameters:\n",
    "    - wind (DataFrame): The window of PPG data.\n",
    "    - pearson_min_mean (float): Minimum mean correlation value.\n",
    "    - th (float): Threshold for auto-correlation peaks (default is 0.7).\n",
    "\n",
    "    Returns:\n",
    "    - tuple: Boolean indicating if the signal is distorted, and the distortion conditions for max and min peaks.\n",
    "    \"\"\"\n",
    "    distorted_max = Distorted_1_Identification(wind['is_max_peak'], wind['NoAuCo_PLETH'], th=th)\n",
    "    distorted_min = Distorted_1_Identification(wind['is_min_peak'], wind['NoAuCo_PLETH'], th=th)\n",
    "\n",
    "    if distorted_min == 0 or distorted_max == 0:\n",
    "        distorted = False\n",
    "    elif distorted_min == 1 or distorted_max == 1:\n",
    "        distorted = pearson_min_mean < th\n",
    "    else:\n",
    "        distorted = True\n",
    "\n",
    "    return distorted, distorted_max, distorted_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d2fc3b4-7679-4397-bd84-b77ec83dc88f",
   "metadata": {
    "id": "4d2fc3b4-7679-4397-bd84-b77ec83dc88f"
   },
   "outputs": [],
   "source": [
    "def Distorted_1_Identification(is_peak, AuCo, padding=100, th=0.7):\n",
    "    \"\"\"\n",
    "    Identifies if the PPG signal is distorted based on auto-correlation peaks.\n",
    "\n",
    "    Parameters:\n",
    "    - is_peak (Series): Boolean series indicating the positions of peaks.\n",
    "    - AuCo (Series): Auto-correlation values of the PPG signal.\n",
    "    - padding (int): Padding value for auto-correlation (default is 100).\n",
    "    - th (float): Threshold for auto-correlation peaks (default is 0.7).\n",
    "\n",
    "    Returns:\n",
    "    - int: Distortion condition (0 for no distortion, 1-3 for different types of distortion).\n",
    "    \"\"\"\n",
    "    is_peak = is_peak.iloc[padding // 2 : -padding // 2]\n",
    "    AuCo = AuCo.iloc[padding // 2 : -padding // 2]\n",
    "\n",
    "    ind_peaks_in_AuCo, _ = signal.find_peaks(AuCo, prominence=0.15)\n",
    "    ind_peaks_in_AuCo = AuCo.index[ind_peaks_in_AuCo]\n",
    "\n",
    "    peak_index = is_peak[is_peak].index\n",
    "\n",
    "    distorted = 0\n",
    "    for i0, i1 in zip(peak_index[:-1], peak_index[1:]):\n",
    "        bool_ind = np.logical_and((ind_peaks_in_AuCo > i0), (ind_peaks_in_AuCo <= i1))\n",
    "        peaks_in_sub_AuCo = ind_peaks_in_AuCo[bool_ind]\n",
    "\n",
    "        sub_AuCo = AuCo.loc[i0:i1]\n",
    "        if len(peaks_in_sub_AuCo) == 1:\n",
    "            peak_ind = peaks_in_sub_AuCo[0]\n",
    "            if sub_AuCo.loc[peak_ind] < th:\n",
    "                distorted = 1\n",
    "                break\n",
    "        elif len(peaks_in_sub_AuCo) == 0:\n",
    "            distorted = 2\n",
    "            break\n",
    "        elif len(peaks_in_sub_AuCo) > 1:\n",
    "            distorted = 3\n",
    "            break\n",
    "    return distorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69a8a3e8-844e-4421-9141-1a4edc597cc0",
   "metadata": {
    "id": "69a8a3e8-844e-4421-9141-1a4edc597cc0"
   },
   "outputs": [],
   "source": [
    "# Initialize the Windows_Table\n",
    "Windows_Table = pd.DataFrame()\n",
    "\n",
    "# Initialize window_id\n",
    "window_id = 0\n",
    "\n",
    "# Windows that pass these checks \n",
    "# Initialize an empty dictionary to store proper windows\n",
    "proper_windows = {}\n",
    "\n",
    "# Process each PPG data segment in wind_dict\n",
    "for key, PPG in valid_segments_dict.items():\n",
    "    Windows_Table, window_id = FindProperWindows(PPG, Windows_Table, key, window_id, proper_windows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2a6cb664-ca80-466a-90e7-76e8f54c4b9a",
   "metadata": {
    "id": "2a6cb664-ca80-466a-90e7-76e8f54c4b9a",
    "outputId": "f38a0455-afc3-447f-f0e8-9abe9b289770"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>window_id</th>\n",
       "      <th>segment_index</th>\n",
       "      <th>start_index</th>\n",
       "      <th>end_index</th>\n",
       "      <th>ProperWindow</th>\n",
       "      <th>Cycles_Number</th>\n",
       "      <th>Corr_Min_Mean</th>\n",
       "      <th>Distorted_By_Corr_Min_Mean</th>\n",
       "      <th>Autocorr_Max_Cond</th>\n",
       "      <th>Autocorr_Min_Cond</th>\n",
       "      <th>Distorted_By_Autocorr</th>\n",
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       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>644</td>\n",
       "      <td>631</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>0.902089</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>645</td>\n",
       "      <td>632</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>0.849899</td>\n",
       "      <td>False</td>\n",
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       "      <th>646</th>\n",
       "      <td>646</td>\n",
       "      <td>633</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "      <td>0.840084</td>\n",
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       "<p>649 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     window_id  segment_index  start_index  end_index  ProperWindow  \\\n",
       "0            0              0            0       1200         False   \n",
       "1            1              1            0       1200         False   \n",
       "2            2              2            0       1200         False   \n",
       "3            3              3            0       1200         False   \n",
       "4            4              4            0       1200          True   \n",
       "..         ...            ...          ...        ...           ...   \n",
       "644        644            631            0       1200          True   \n",
       "645        645            632            0       1200          True   \n",
       "646        646            633            0       1200         False   \n",
       "647        647            634            0       1200          True   \n",
       "648        648            635            0       1200         False   \n",
       "\n",
       "     Cycles_Number  Corr_Min_Mean  Distorted_By_Corr_Min_Mean  \\\n",
       "0                9       0.272463                        True   \n",
       "1               11       0.495988                       False   \n",
       "2               10       0.105909                        True   \n",
       "3               11       0.605685                       False   \n",
       "4               11       0.912021                       False   \n",
       "..             ...            ...                         ...   \n",
       "644             11       0.902089                       False   \n",
       "645             13       0.849899                       False   \n",
       "646             12       0.555389                       False   \n",
       "647             12       0.840084                       False   \n",
       "648             12       0.475807                       False   \n",
       "\n",
       "     Autocorr_Max_Cond  Autocorr_Min_Cond  Distorted_By_Autocorr  \n",
       "0                    1                  1                   True  \n",
       "1                    2                  1                   True  \n",
       "2                    1                  1                   True  \n",
       "3                    3                  2                   True  \n",
       "4                    0                  3                  False  \n",
       "..                 ...                ...                    ...  \n",
       "644                  1                  1                  False  \n",
       "645                  0                  0                  False  \n",
       "646                  1                  1                   True  \n",
       "647                  3                  0                  False  \n",
       "648                  3                  1                   True  \n",
       "\n",
       "[649 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Windows_Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7e588f94-7aa8-400e-803d-af07d372577f",
   "metadata": {
    "id": "7e588f94-7aa8-400e-803d-af07d372577f",
    "outputId": "b1532321-c003-4098-95ca-33d4dfd652e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numbers of windows checked: 649\n",
      "Number of proper windows in dict: 277\n",
      "Number of Proper Windows in the windows table: 277\n"
     ]
    }
   ],
   "source": [
    "# Display the number of proper windows for checking:\n",
    "print('numbers of windows checked:',len(Windows_Table))\n",
    "print(\"Number of proper windows in dict:\", len(proper_windows))\n",
    "print(f\"Number of Proper Windows in the windows table: {Windows_Table['ProperWindow'].sum()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea3e874",
   "metadata": {},
   "source": [
    "**After processing, 277 proper windows were identified out of 649 windows derived from the 636 valid segments. Each segment was broken down into 12-second windows, and only 277 of these windows met the strict criteria to be considered proper for further analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b4ac2ed8-b005-41a8-9a17-b017c2298dcf",
   "metadata": {
    "id": "b4ac2ed8-b005-41a8-9a17-b017c2298dcf"
   },
   "outputs": [],
   "source": [
    "# Iterate through each DataFrame in the proper_windows dictionary and keep only the specified columns\n",
    "for key in proper_windows:\n",
    "    proper_windows[key] = proper_windows[key][['TIMESTAMP_MS', 'PLETH', 'is_max_peak', 'is_min_peak']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "791ddb9a-bd46-43ad-b2d3-3d6c0a428b9f",
   "metadata": {
    "id": "791ddb9a-bd46-43ad-b2d3-3d6c0a428b9f",
    "outputId": "aff7a6ed-23aa-4ead-fc38-4253fd3e00c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'example of proper window')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(proper_windows[4]['PLETH'])\n",
    "plt.title(\"example of proper window\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4686a870-ceb7-456f-8fca-0cb2894ff6b5",
   "metadata": {
    "id": "4686a870-ceb7-456f-8fca-0cb2894ff6b5",
    "outputId": "d6086a23-6152-41e8-bc64-6d3f70cf84f7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_ppg_processing_steps(wind):\n",
    "    \"\"\"\n",
    "    Plots the steps of PPG signal processing.\n",
    "\n",
    "    Parameters:\n",
    "    - wind (DataFrame): The window of PPG data.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(6,4))\n",
    "    # Plot the raw PLETH signal\n",
    "    plt.plot(range(len(wind['PLETH'])),wind['PLETH'], label='Raw PLETH')\n",
    "    plt.title('PPG segment')\n",
    "    plt.xlabel('TS (10 ms)')\n",
    "    plt.ylabel('Amplitude')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    # Apply and plot the filtered PLETH signal\n",
    "    plt.figure(figsize=(6,4))\n",
    "    filtered = Segment_Filter(wind['PLETH'], ftype='low')\n",
    "    plt.plot(range(len(wind['PLETH'])),wind['PLETH'],label='Raw PLETH')\n",
    "    plt.plot(filtered, label='Filtered PLETH')\n",
    "    plt.title('PPG segment (Raw vs Filtered)')\n",
    "    plt.xlabel('TS (10 ms)')\n",
    "    plt.ylabel('Amplitude')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    #zoom in:\n",
    "    plt.figure(figsize=(6,4))\n",
    "    plt.plot(range(800, 1100), wind['PLETH'][800:1100], label='Raw PLETH')\n",
    "    plt.plot(range(800, 1100), filtered[800:1100], label='Filtered PLETH')\n",
    "    plt.ylim(58000, 58400)\n",
    "    plt.title('PPG Segment (Raw vs Filtered) - Zoomed In')\n",
    "    plt.xlabel('Sample Number')\n",
    "    plt.ylabel('Amplitude')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    # Apply and plot the trend-removed PLETH signal\n",
    "    plt.figure(figsize=(6,4))\n",
    "    detrended = Segment_Trend_Removel(filtered, ftype='high')\n",
    "    plt.plot(filtered, label='Filtered PLETH')\n",
    "    plt.plot(detrended, label='Trend-Removed PLETH')\n",
    "    plt.title('PPG segment (Trend-Removed vs filtered')\n",
    "    plt.xlabel('TS (10 ms)')\n",
    "    plt.ylabel('Amplitude')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot_peaks(wind):\n",
    "    \"\"\"\n",
    "    Plots the detected peaks on the detrended PLETH signal.\n",
    "\n",
    "    Parameters:\n",
    "    - wind (DataFrame): The window of PPG data.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.plot(wind['DataTime'], wind['HiDeTr_PLETH'], label='Trend-Removed PLETH')\n",
    "    plt.scatter(wind['DataTime'][wind['is_max_peak']], wind['HiDeTr_PLETH'][wind['is_max_peak']], color='r', marker='^', label='Max Peaks')\n",
    "    plt.scatter(wind['DataTime'][wind['is_min_peak']], wind['HiDeTr_PLETH'][wind['is_min_peak']], color='g', marker='v', label='Min Peaks')\n",
    "    plt.title('Detected Peaks on Trend-Removed PLETH Signal')\n",
    "    plt.xlabel('Time (s)')\n",
    "    plt.ylabel('Amplitude')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Select a sample window\n",
    "sample_window_id = 4  # Adjust the window ID as needed\n",
    "sample_window = valid_segments_dict[sample_window_id]\n",
    "\n",
    "# Process the sample window to generate the required columns\n",
    "WindowFullProcess(sample_window, by_value=False, wind_id=sample_window_id)\n",
    "\n",
    "# Plot the steps of processing\n",
    "plot_ppg_processing_steps(sample_window)\n",
    "plot_peaks(sample_window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3006234f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new figure with a specified size for better visualization\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Plot the 'PLETH' values from the first segment in the valid_segments_dict\n",
    "plt.plot(valid_segments_dict[0]['PLETH'])\n",
    "\n",
    "# Set the title of the plot to indicate that this is an example of a rejected window\n",
    "plt.title(\"Example of Rejected Window\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9a37642b-d2d0-4185-84ba-8e5a91860d07",
   "metadata": {
    "id": "9a37642b-d2d0-4185-84ba-8e5a91860d07",
    "outputId": "bddfede7-a578-410d-c000-bbdb5146473c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary saved to C:/Users/adior/Documents/Digital Medical Tech - degree/Final Mop Project/Train Files/Proper Windows\\proper_windows.pkl\n"
     ]
    }
   ],
   "source": [
    "# Define the directory path where you want to save the files\n",
    "directory_path = 'C:/Users/adior/Documents/Digital Medical Tech - degree/Final Mop Project/Train Files/Proper Windows'\n",
    "#directory_path = 'C:/Users/Idan Lichter/OneDrive/Desktop/study/3rd year/Final Project/Proper Windows'\n",
    "\n",
    "file_path = os.path.join(directory_path, 'proper_windows.pkl')\n",
    "\n",
    "# Create the directory if it does not exist\n",
    "os.makedirs(directory_path, exist_ok=True)\n",
    "\n",
    "# Save the dictionary to a pickle file\n",
    "with open(file_path, 'wb') as f:\n",
    "    pickle.dump(proper_windows, f)\n",
    "\n",
    "print(f\"Dictionary saved to {file_path}\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
